Conceptually, a computing system (e.g., a computing device, a personal computer, a laptop, a Smartphone, a mobile phone) can accept information (content or data) and manipulate it to obtain or determine a result based on a sequence of instructions (or a computer program) effectively describing how to process the information. Typically, the information is stored in a computer readable medium in a binary form. More complex computing systems can store content including the computer program itself. A computer program may be invariable and/or built into, for example, a computer (or computing) device as logic circuitry provided on microprocessors or computer chips. Today, general purpose computers can have both kinds of programming. Storing content (or information retention) is of one the core functions of computing devices. Today, numerous types of computing devices are available. These computing devices widely range with respect to size, cost, amount of storage and processing power. The computing devices that are available today include: expensive and powerful servers, relatively cheaper Personal Computers (PC's) and laptops, and yet less expensive microprocessors (or computer chips) provided in storage devices, automobiles, and household electronic appliances. Memory and storage devices have long been an important part of computing and computing devices. Memory and storage device technology have been markedly improved in recent years. Today, relatively large amounts of content can be stored in portable and easy to access devices such as USB flash drives (or USB flash devices). The content stored on USB flash drives can be easily carried and copied on to various computing devices. By way of example, data stored on a server computer can be copied on to a USB flash drive and taken home and loaded (or copied) on to a Personal Computer (PC).
Among other things, computing systems can be used for image processing. Generally, image processing can be any form of signal processing where the input is an image (e.g., photographs, frames of video). The output of image processing can also be an image or a set of characteristics or parameters related to the image. Image processing techniques can involve treating the input image as a two-dimensional signal and applying standard signal-processing techniques to it. Image processing techniques can also be used to process a 3-D image. An image can be represented as a graph which can be “traversed” in order to process the image. During graph traversal, information can be propagated between the nodes of the graph.
Often, image processing refers to digital image processing as the use of computer (or computer-based) algorithms to process digital images. As a subfield of digital signal processing, digital image processing has many advantages over analog image processing, as it allows a wider range of algorithms to be applied to the input, as well as avoiding various problems associated with analog image processing (e.g., buildup of noise, signal distortion during processing).
Digital image processing is widely recognized by the scientific community and used by those in the general public accustomed to digital cameras, digital image editing, and the like. As such, digital image processing techniques are very useful as evidenced by their use in various forms and in many different applications. Digital imaging can be realized or achieved using a computing system (e.g., a computer). For example, a computer can execute (or run) an executable computer program to recognize objects in various digital images stored in a database.
In digital image processing, an image (I) can be represented by a graph “G=(V, E)” where “V” represents a set of nodes and “E” represents the edges of the graph. In the graph “G=(V, E)” each vertex v is a subset of nodes (V) (vεV) that can, for example, represent a pixel of the image (I) or a set of pixels in the image (I). The edges (E) can represent the relationship between the pixels or pixel groups. By way of example, the image (I) can be represented by performing the following operations: (i) for each pixel in the image (I), use a vertex to represent a pixel, (ii) for each pixel vertex, connect it with its north, south, east, and west neighboring pixel vertices by edges. The result is a connected graph representing the image (I) with neighboring relationships of its pixels. Similarly, a 3D image can be also represented by a graph with vεV representing a voxel or a set of voxels with a set of edges (E) representing the relationship between the voxels or voxel groups. Given a graph G=(V, E) and a set of source vertices (S) as a subset of the nodes (V) (S⊂V) “graph traversal” can refer to “walking” from the source vertices (S) to other vertices following the edges (E) of the graph until a termination criterion is satisfied (e.g., all the vertices in V have been visited).
A Breadth First Search (BFS) graph traversal algorithm can be defined as follows. Let u and v be two vertices in the graph “G=(V, E),” u and v are neighbors if and only if there exists an edge connecting vertex u and vertex v. For a vertex u, let “N(u)” be the set of neighboring nodes of u. For a set of vertices U, let
  “            N      ⁡              (        U        )              =                  ⋃                  u          ∈          U                    ⁢                                    N            ⁡                          (              u              )                                ”                .            Given a graph “G=(V, E)” and a set of source vertices S ⊂V, a BFS graph traversal algorithm can be performed by traversing the graph iteratively, such that for the first iteration, all vertices in “N(S)” are visited and for the second iteration “N(N(S))” is visited, and for the third iteration “N(N(N(S)))” is visited, and so on. It should be noted that a graph “G=(V, E)” can be used by other image processing techniques, such as, for example, “watershed” algorithms that propagate labels on a number of finite numbers of layers in order to detect boundaries as generally known in the art.
Digital image processing can require many computations. As such, digital image processing can be a computationally intensive process. Object recognition, which generally requires graph traversal, is an example of a digital imaging process that can be very computationally intensive. As a key technique for analyzing images, object recognition, among other things, can be used to classify objects in digital images. The digital images can, for example, be stored in a database. However, more accurate object recognition techniques are relatively more computationally intensive, as is generally known in the art.
Given that digital imaging is widely used for numerous applications today, alternative techniques for image processing would be useful.